Machine learning in astronomy : possibilities and pitfalls : proceedings of the 368th Symposium of the International Astronomical Union, Busan, Republic of Korea, 2-4 August, 2022 / edited by Jess McIver, Ashish Mahabal, and Christopher Fluke.

Format:

Book

Author/Creator:

International Astronomical Union. Symposium (368th : 2022 : Pusan, Korea), author.

Contributor:

McIver, Jess, editor.

Mahabal, Ashish, editor.

Fluke, Christopher, editor.

Series:

IAU symposium and colloquium proceedings series

IAU Symposium proceedings series

Language:

English

Subjects (All):

Astronomy--Data processing--Congresses.

Astronomy.

Machine learning--Congresses.

Machine learning.

Physical Description:

viii, 137 pages : illustrations, charts ; 25 cm.

Place of Publication:

Cambridge, United Kingdom ; New York, USA : Cambridge University Press, 2025.

Summary:

"Today's astronomical observatories are generating more data than ever, from surveys to deep images. Machine learning methods can be a powerful tool to harness the full potential of these new observatories, as well as large archives that have accumulated. However, users should beware of common pitfalls, including bias in data sets and overfitting. IAU Symposium 368 addresses graduate students, teachers and professional astronomers who would like to leverage machine learning to unlock these huge volumes of data. Researchers pushing the frontiers of these methods share best practices in applied machine learning. While this volume is focused on astronomy applications, the methodological insights provided are relevant to all data-rich fields. Machine learning novices and expert users will find and benefit from these fresh new insights"--Back cover.

Contents:

Enhancing exoplanet surveys via physics-informed machine learning / Eric B. Ford

How do we design data sets for machine learning astronomy? / Renée Hložek

Deep machine learning in cosmology: evolution or revolution? / Ofer Lahav

An astronomers guide to machine learning / Sara A. Webb and Simon R. Goode

Panel discussion: practical problem solving for machine learning / Guillermo Cabrera, Sungwook E. Hong, Lilianne Nakazono, David Parkinson and Yuan-Sen Ting

Panel discussion: methodology for fusion of large datasets / Nikhita Madhanpall, Kai Polsterer, Mike Walmsley and Shay Zucker

The entropy of galaxy spectra / I. Ferreras, O. Lahav. R. S. Somerville and J. Silk

Unsupervised classification: a necessary step for deep learning? / Didier Fraix-Burnet

Spectral identification and classification of dusty stellar sources using spectroscopic and multiwavelength observations through machine learning / Sepideh Ghaziasgar, Amirhossein Masoudnezhad, Atefeh Javadi, Jacco Th. van Loon, Habib G. Khosroshahi and Negin Khosravaninezhad

Simulating transient burst noise with gengli / Melissa Lopez, Vincent Boudart, Stefano Schmidt and Sarah Caudill

Detecting complex sources in large surveys using an apparent complexity measure / David Parkinson and Gary Segal

Machine learning in the study of star clusters with Gaia EDR3 / Priya Hasan, Md Mahmudunnobe, Mudasir Raja, Md Saifuddin and S N Hasan

Assessing the quality of massive spectroscopic surveys with unsupervised machine learning / John F. Suárez-Pérez and Jaime Forero-Romero

Neural networks for meteorite and meteor recognition / Aisha Al-Owais, Maryam Sharif, Ilias Fernini, Antonios Manousakis

Unsupervised clustering visualisation tool for Gaia DR3 / Marco Álvarez, Carlos Dafonte, Minia Manteiga, Daniel Garabato, Raúl Santoveña and Lara Pallas

Kinematic Planetary Signature Finder (KPSFinder): convolutional neural network-based tool to search for exoplanets in ALMA data / Jaehan Bae

Predicting physical parameters of Cepheid and RR Lyrae variables in an instant with machine learning / A. Bhardwaj, E. P. Bellinger, S. M. Kanbur and M. Marconi

Bayesian deconvolution of a rotating spectral line profile to a non-rotating one / M. Curé, P. Escarate, L. Celedon, J. Cavieres, E. Olivares, I. Araya, C. Arcos, R. Pezoa, G. Farias and N. Machuca

A short study on the representation of gravitational waves data for convolutional neural network / M. Grespan

Search for microlensing signature in gravitational waves from binary black hole events / Kyungmin Kim

Deep learning and numerical simulations to infer the evolution of MaNGA galaxies / Regina Sarmiento, Johan H. Knapen, Marc Huertas-Company, Annalisa Pillepich, Sebastián F. Sánchez, Héctor Ibarra-Medel and Eduardo Lacerda

Data pre-extraction for better classification of galaxy mergers / W. J. Pearson, L. E. Suelves, NEP Team and GAMA Team

Stellar spectra classification and clustering using deep learning / Tomasz Różański

Is GMM effective in membership determination of open clusters? / S. N. Hasan, Md Mahmudunnobe, Priya Hasan and Mudasir Raja

Deep radio image segmentation / Hattie Stewart, Mark Birkinshaw and Jason Yeung

Computational techniques for high energy astrophysics and medical image processing / Nicolás Vásquez, Jennifer Ortega, David Erazo, Ricardo Caiza and Orlando Gutiérrez

Deep learning proves to be an effective tool for detecting previously undiscovered exoplanets in Kepler data / Amelia M. Yu.

Notes:

Includes bibliographical references and index.

ISBN:

1009345192

9781009345194

OCLC:

1477211859

Publisher Number:

90103495903

CIPO000217652